This article was published as a part of the Data Science Blogathon. This is a multiclass classification project to classify the severity of road accidents into three categories. This project is based on real-world data, and the dataset is also highly imbalanced. There are three types of injuries in a target variable: minor, severe, and fatal. Road accidents are the major cause of unnatural deaths around the world.

I used machine-learning techniques to classify four different types of brain tumors; No Tumor, Glioma Tumor, Meningioma Tumor, and Pituitary Tumor. So, I thought of writing an article about the same. I will divide this article into two different parts, In part 1 we will build a machine-learning model to classify the brain tumor (the one you're reading), and in part 2 I will talk about how to put your machine-learning model into production. So, without any further ado, let's get started. A Brain Tumor is nothing but a mass or growth of abnormal cells in your brain.

The two codes above are similar to what you guys might have seen before in developing a Machine Learning Model. The first object, loss_fn, is created to measure the error rate from the predicted results, while the second object, optimizer, is to reduce the loss in every iteration of the training.

Today you'll learn about a very powerful library called Numpy. We'll learn about Numpy Array(np array for short) and operations on them, along with what makes them better than the pre-existing data structures. The most important entity in the whole NumPy package is the Numpy Array. If you've worked with Python before you must be familiar with the data structure called Lists. List are containers that can store any kind of data in it.

You've built your Machine Learning model with 99% accuracy and now you are ecstatic. Then you paused and you were like – now what? Well first, you might have thought of uploading your code to GitHub and showing people your Jupyter notebook file. It comprises those gorgeous-looking visualizations you created using Seaborn, those extremely powerful ensemble models, and how they are able to pass their evaluation metrics and so on. But then you noticed that no one is interacting with it.

We all now how boring it gets after a while to attend online classes. So, I made an AI to attend them for me. Let's see how will the AI get the data from the class? So from the above image I hope you get the basic gist of how the data collection will work. So, basically what the above code does is take the screenshoted image and make it negative so that the black turns white and the white turns black and then detect the the text and draw rectangles around it on the original image.

The most common type of Autoencoder is an Undercomplete Autocencoder which squeezes (encodes) data into fewer neurons (lower dimension) while removing "unimportant" information. It achieves that by training an encoder and decoder simultaneously, so the output neurons match inputs as closely as possible.

Project Summary: Build deep neural network models that can analyze satellite images for disaster. Below are some examples of the images downloaded. The image on the right is before the disaster and the one on the right is the same region after the disaster. As you have probably understood, the data is in pairs. Every region has a pre-disaster image and a post-disaster image.

In this Python tutorial, we will learn How Scikit learn confusion matrix works in Python and we will also cover different examples related to Scikit learn confusion matrix. And, we will cover these topics. In this section, we will learn about how the Scikit learn confusion matrix works in python. After running the above code, we get the following output in which we can see that the confusion matrix value is printed on the screen. In this section, we will learn about how Scikit learn confusion matrix example works in python.

PyTorch is one of the most used frameworks in the field of deep learning. We can use this library in every aspect and field data science and machine learning. We can also use it for reinforcement learning. In one of our articles, we have discussed reinforcement learning and the procedure that can be followed for building reinforcement learning models using TensorFlow in detail. In this article, we will discuss how we can build reinforcement learning models using PyTorch.